DBMS > GraphDB vs. Neo4j vs. PostgreSQL

System Properties Comparison GraphDB vs. Neo4j vs. PostgreSQL

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Editorial information provided by DB-Engines

Name

Description

Enterprise-ready RDF and graph database with efficient reasoning, cluster and external index synchronization support. It supports also SQL JDBC access to Knowledge Graph and GraphQL over SPARQL.

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Graph DBMS
RDF store

Graph DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	2.57
Rank	#95	Overall
	#7	Graph DBMS
	#3	RDF stores

Score	47.59
Rank	#20	Overall
	#1	Graph DBMS

Score	667.25
Rank	#4	Overall
	#4	Relational DBMS

Website

www.ontotext.com

neo4j.com

www.postgresql.org

Technical documentation

graphdb.ontotext.com/documentation

neo4j.com/docs

www.postgresql.org/docs

Developer

Ontotext

Neo4j, Inc.

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2000

2007

1989

1989: Postgres, 1996: PostgreSQL

Current release

10.4, October 2023

5.23, August 2024

16.4, August 2024

License

Commercial or Open Source

commercial

Some plugins of GraphDB Workbench are open sourced

Open Source

GPL version3, commercial licenses available

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Scala

Server operating systems

All OS with a Java VM
Linux
OS X
Windows

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

schema-free and OWL/RDFS-schema support; RDF shapes

schema-free and schema-optional

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes, supports real-time synchronization and indexing in SOLR/Elastic search/Lucene and GeoSPARQL geometry data indexes

yes

pluggable indexing subsystem, by default Apache Lucene

yes

SQL

Support of SQL

stored SPARQL accessed as SQL using Apache Calcite through JDBC/ODBC

yes

standard with numerous extensions

APIs and other access methods

GeoSPARQL
GraphQL
GraphQL Federation
Java API
JDBC
RDF4J API
RDFS
RIO
Sail API
Sesame REST HTTP Protocol
SPARQL 1.1

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.Net
C#
Clojure
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

well-defined plugin interfaces; JavaScript server-side extensibility

yes

User defined Procedures and Functions

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

via event handler

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes using Neo4j Fabric

partitioning by range, list and (since PostgreSQL 11) by hash

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Causal Clustering using Raft protocol

available in in Enterprise Version only

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency, Eventual consistency (configurable in cluster mode per master or individual client request)

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Immediate Consistency

Foreign keys

Referential integrity

yes

Constraint checking

yes

Relationships in graphs

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Default Basic authentication through RDF4J client, or via Java when run with cURL, default token-based in the Workbench or via Rest API, optional access through OpenID or Kerberos single sign-on.

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

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22 April 2025

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14 April 2025

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14 April 2025

This Week in Neo4j: NODES, GraphRAG, Agents, Knowledge Graph and more
12 April 2025

Google Cloud & Neo4j: Teaming Up at the Intersection of Knowledge Graphs, Agents, MCP, and Natural Language Interfaces
9 April 2025

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